Climate Simulation Using RCM Data for Jamnagar District

Introduction Climate change is also one of the threats among several other impacting on water resources. Scarcity of water resources, pollution and climate change will be the major emerging issues in the current and next century. Climate change and global warming is the result of a build-up of greenhouse gases (GHG), chiefly carbon dioxide, in the atmosphere. Global climate models (GCMs) are fundamental tools for predicting future climate to enable developing a better understanding of climate change9. Regional Abstract Scarcity of water resources and pollution will be the major emerging issues in the current and next century. Climate change is also one of the threats among several other impacting on water resources. GCMs are fundamental tools for predicting future climate and RCMs are outstanding tools for studying the mechanisms of climate at scales that are not yet resolved by GCM. The meteorological data (precipitation and temperature) simulated by CGCM2.3.2 RCM for the control period (1961-2000) as well as future periods (2046-64 & 1981-2100) were analyzed for the bias corrections. It was analyzed to assess how well the important statistics (Coefficient of Variation and Mean) of the bias corrected for four grid points of RCM simulated data match those of the observations. The bias corrected RCM simulated rainfall and temperature were found increasing from 1961 to 2100 and noticed that the warming will be more due to increase in minimum temperature rather than maximum temperature. Article History

climate models (RCMs) are outstanding tools for studying the mechanisms of climate at scales that are not yet resolved by general circulation models (GCM).RCMs are still prone to biases and the simulated climate is not always fully consistent with the observations, which is critical in climate change impact research 14 .Several methodologies have been recently proposed and evaluated, mostly focused on precipitation and temperature 1,2,8,13,16 .The study area is Jamnagar district of Saurashtra region of Gujarat.It extends from 21º40' N to 22º57'N

Materials and Methods
The bias correction of RCM simulated data was made to match with the observed weather (daily Precipitation and maximum/minimum temperature) for the base line period-1961-2000.Accordingly, the bias correction was applied for the future scenarios-2046-64 and 2081-2100.The mean and coefficient of variation (CV) for the bias correction was applied to the daily minimum/maximum temperature and precipitation on monthly window using the auto correlation by Linear Transformation and & Variance Scaling for temperature and power Transformation for precipitation.The RCM simulated data were available for 1961-2000 as control period and 2046-64 and 2081-2100 as future predictions.Therefore, for the bias corrections, the period 1967-2000 and 1976-2000 were taken as baseline periods for the temperature and precipitation respectively.The RCM simulated and observed data were compared on monthly window and the statistical parameters namely mean and standard deviations were determined for each of 12 months separately such that both could be matched on monthly windows.The RCM simulated data of the rest periods were bias corrected using the parameters obtained during base line period.The methodology described by 17 was used for the bias correction.

Linear Scaling Approach for Precipitation and Temperature
This approach worked for Monthly correction based on the differences between observed and presentday simulated values 12 .

Power Transformation of Precipitation
The variance statistics of a precipitation time series adjust by ap b10, 11 .First, b was acknowledged by corresponding the CVof the corrected daily RCM precipitation (P b ) with the CV of observed daily precipitation (Pobs) for each month m.The value of bm was taken as According to Brent's method 3 it is done byroot-finding algorithm.By using the standard linear scaling parameter the long-term monthly mean of observed precipitation was matched with the monthly mean of the intermediary series

Variance Scaling of Temperature
The mean and the variance effectively corrected by Power transformation.Due to the use of a power function, it is limited to precipitation time series.The means (ìm) of the RCM-simulated time series were adjusted by linear scaling (Eqs.( 3) and (4) 4,5 Meanwhile, the mean-corrected control and scenario runs were shifted on a monthly basis to a zero mean: Then, Based on the ratio of observed óand controlrun σ the standard deviations of the shifted time series ( )

Results and Discussion Precipitation for Control Scenario (1976-2000)
The Fig. 1 shows that the RCMs simulated daily precipitations were found overestimated over actual observation during May to July.However, after applying bias correction, the monthly daily mean, standard deviation and coefficient of variation of daily precipitation for each of 12 months was exactly matched with those of respective observed data for the base line period of 1976-2000.The uncorrected RCMs precipitation had a positive (Over Estimated) bias from May and June and biases for the July and August months were negligible.In comparison to the corrected RCMs output, the uncorrected monthly mean precipitation values were higher during May and June months and nearly same during July and September month.The effect of sampling variability was reduced by determining the parameters a andb for every month of the year using the distribution free approach.performed using raw data without bias correction, RCMs showed a higher amount of disagreement during April and May months in 2046-64 (Fig. 3).By the bias correction of RCM simulated precipitation, the monthly mean of the daily corrected values were reduced over simulated raw values during the April and May months.For the rest of months, the values did not differed much.

Precipitation for Future Scenario (2081-2100)
In Fig. 4, the bias corrections of simulated raw data reduced the precipitation amounts than of original amount simulated by RCM during April and May Months.Meanwhile, during August and October Months the corrected precipitation was higher than that of uncorrected.When the analysis was performed using raw data without bias correction, RCM simulations showed a large amount of disagreement.Thus, the range of projected future percentage changes in precipitation differed a lot from May to August.On an average, precipitation is expected to increase during July and August as compared to RCMs simulated, whereas it is projected to nearly same for all other seasons.The largest corrections were required in May, for which the mean precipitation was reduced to 1.97 from 8.45.

Minimum Temperature for Control Scenario (1967-2000)
Figure 6 shows that the RCMs simulated minimum temperature were overestimated during January to May and December months.After applying the bias correction of Linear Transformation and Variance Scaling methods, the mean and CV of the daily minimum temperature were agreed well with the actual observations for whole year.It was found that the RCM in simulating the daily minimum temperature had a positive bias from January to May and December months.
Minimum Temperature for Future Scenario (2046-2064) Fig. 7 shows that the RCM simulated daily minimum temperature higher during the months of January to May and December which required to be bias corrected.While during the rest of the months, the uncorrected and corrected daily minimum     The Fig. 12 shows that the RCM simulated the maximum temperature higher during January-April and September while lower during June, July and November and rest the months did not differed much.In fact, the biases in simulating the maximum temperature during the entire year could not found much.
finally, the σ-corrected time series (

Fig. 1 :
Fig. 1: Comparison of monthly mean of uncorrected RCM, bias corrected RCM and observed daily precipitation during the control period (1976-2000).Determination of the parameter b was done iteratively, so that the coefficient of variation of the daily precipitation values predicted by RCM matches the coefficient of variation (CV) of the observed monthly precipitation.Parameter a was determined such that the mean of the transformed daily values of precipitation matched with the observed mean.A depended on b and b depended only on CV 15 .

Fig. 2 Fig. 2 :
Fig.2 displays the annual cycle of the coefficient a and exponent b for four grid points of RCM run for the control period.

Fig. 3 :
Fig. 3: The comparison of monthly mean of daily uncorrected and corrected precipitation simulated by RCM for the future period 2046-2064.

Fig. 4 :
Fig. 4: The comparison of monthly mean of daily uncorrected and corrected precipitation simulated by RCM for the future period 2081-2100.Comparison of Precipitation for Control (1976-2000) and Future Scenarios (2046-2064 and 2081-2100) Fig. 5 showed that the monthly mean of daily precipitation in the months of April to July would be increased during 2046-64 as compared to base line period-1976-2000.During future-2081-2100, the months of April, July, August, September and October were increased as compared to period of 2046-64 and base line period-1976-2000.During 2081-2100, in June Months is reduced over period of 2046-64.However, it will be highest during the period-2081-2100 followed by 1961-2000 and 2046-64 during the months of July to October.Therefore, it can be said that the rainfall during July to October will be reduced during the period 2046-64 as compared to base line period-1961-200 and again it will be increased during the period-2081-2100.There can be no much impacts on rainfall during the Months of January to March, November and December.

Fig. 6 :
Fig. 6: The comparison of monthly mean of daily observed, uncorrected and bias corrected minimum temperature for the base line period (1967-2000).

Fig. 7 :
Fig. 7: the comparison of monthly mean of RCM simulated daily uncorrected and bias corrected minimum temperature for the future scenario (2046-64).

Fig. 8 :
Fig. 8: the comparison of monthly mean of RCM simulated daily uncorrected and bias corrected minimum temperature for the future scenario (2081-2100).

Fig. 10 :Fig. 11 :
Fig. 10: The comparison of RCM simulated corrected and uncorrected daily maximum temperature during the control period 1978-2000

Fig. 12 :
Fig. 12: The comparison of RCM simulated corrected and uncorrected daily maximum temperature during the control period 2081-2100.